Given two styles \(A = \lbrace a_1,...,a_m\rbrace \)  and \(B = \lbrace b_1,...,b_n\rbrace \) , where \(m=2n\) , let \(\mathcal {C} = \lbrace a_i : 1 \le i \le n\rbrace \) , \(\mathcal {G}_A = \lbrace a_i : n < i \le 2n\rbrace \) , \(\mathcal {G}_B = B\) , and \(\mathcal {G} = \mathcal {G}_A \cup \mathcal {G}_B\) . By construction \(\mathcal {G} \cap \mathcal {C} = \varnothing \) . We train a Random Forest and compare two distributions \(x = [S^{\mathcal {G},\mathcal {C},\mathcal {F}}_g : g \in \mathcal {G}_A]\)  and \(y = [S^{\mathcal {G},\mathcal {C},\mathcal {F}}_g : g \in \mathcal {G}_B]\) , where \(\mathcal {F}\)  denotes the set of features described in Table REF . Ideally, each value in \(x\)  should be larger than all values in \(y\) , since elements in \(\mathcal {G}_A\)  and \(\mathcal {C}\)  belong to the same style \((A)\) . However, depending on the specificity of the style, there may be some degree of overlap between \(A\)  and \(B\) . In order to determine if there is a measurable difference between \(x\)  and \(y\)  we directly compare the means \((\bar{x} > \bar{y})\) , and we calculate the \(p\) -value \((p^{\bar{x} > \bar{y}})\)  for a One-Sided Mann-Whitney test [1]} with the alternative hypothesis that \(\bar{x} > \bar{y}\) .
